In order to objectively evaluate energy usage, there needs to be an accurate method of normalizing the energy usage to compensate for different environmental effects. For example, it would be beneficial to be able to compare relative energy usage between two buildings in different climates, or from the same building over a time period with different or erratic weather patterns (e.g., varying weather patterns due to climate change). Current methods of normalization such as the “Degree Day” model, Climate Severity Index, the Modified Utilization Factor, and Simulation-Based Weather Normalization all have various benefits and drawbacks. Similarly, methods of normalization that are specific to a given structure such as balance points require complex information that is often difficult to obtain and/or inaccurate. Thus, there exists a need to form accurate methods of normalization. In addition, with the growing use of energy grids (e.g., smart grids) for energy allocation, there is an increased need to forecast energy consumption in order to ensure proper allocation of resources.